I think this is the reason why you have the tendency to propose some freeze-all policies, full control or similar. If you want to find the equilibrium, you need to accept that it will be a controlled equilibrium, most likely on a saddle point, with underlying process changing all the time, requiring fast changes in regulations. Our democratic systems, laws, etc. are not built to do that, they are built on the idea of intrinsic stability of our world where incremental improvements do not need cutting through what was decided before.
https://jodavaho.io/posts/ai-jobpocolypse.html
The difference in the unemployment vs efficient employment model is mostly user driven adoption vs company mandated adoption, or centaurs vs reverse centaurs.
https://pluralistic.net/2026/07/02/canonization/#operate-ite...
> Our democratic systems, laws, etc. are not built to do that, they are built on the idea of intrinsic stability of our world where incremental improvements do not need cutting through what was decided before.
Without totally derailing the thread, this is also obviously why climate and biosphere collapse is not (and likely will continue not) to be addressed, e.g. Timothy Morton's Hyperobjects
because it is. Previously: https://news.ycombinator.com/item?id=43571851 / https://ai-2027.com/
A recursively self-improving AI has strong first-mover effects. That isn’t fundamentally incompatible with commoditisation if there is literally only one path to super-intelligence and you can have AIs at different rings on that ladder co-existing. (Not technically commoditised at that point. There are still different rings. But close enough.)
But the existence of commoditised AI implies model selection isn’t a huge deal, which in turn implies the models are about the same, which strongly implies there is no recursive self-improvement. Depending on your definition, you may still have AGI. But you don’t have superintelligence.
This is only true at a given AI capability level, no? e.g., if AI at the GLM-5.2 level is commoditized, all that suggests is that there's no recursive self-improvement easily possible at the capability level of GLM-5.2. (And with the harnesses for it that exist so far, etc etc.)
If I observe commoditization of a given tier of model capabilities at a given point in time, this seems to say little about what's possible with models six months later, or models that are undergoing proprietary deployments at that very moment inside the major labs, or even models that are notionally available for public use but have had recursive self-improvement adjacent capabilities intentionally nerfed (e.g., Fable).
(I might be misinterpreting your comment tbc - if you mean observing commoditization implies there is no existing, ambient superintelligence at the moment of that observation, then I don't disagree.)
Most of the discussion around AGI is highly speculative. I am not saying AGI could not exist, and it is a term that has historically been loosely defined. Decades of coming science and research will tell.
https://www.tobyord.com/writing/inefficiency-of-reinforcemen...
This is similar to that other exponential, which happened with CPUs - we ran out of true geometric scaling in the mid 2000s, and everything else supporting Moore's Law has been cleverness that arrived in the nick of time, supported by a bit of marketing, and very optimizable benchmarks, far from guaranteed gains coming from making a single physical metric better.
I'm confused if this is satire, sarcasm, or genuine belief. If this was the case, then AI companies should absolutely remove the "it may make mistakes", because doing mistakes would imply that "the very best human knowledge and expertise" is what actually fails, and not the AI.
With that being said, I'll still urge people to visit a professional therapist for health problems and I generally still trust human knowledge workers for critical scenarios. I will reconsider your claim when chatGPT can effectively play Yu-Gi-Oh! (or at the very least respond with the correct rules appropriately), which is a significantly lower stakes scenario than betting your entire company on its aptitude.
For anything health related all AI models show high levels of anchoring bias. I would not use it as a confidant, and be skeptical of claims. Even so, human doctors are also fallible and prone to cognitive bias.
I think the obfuscation is because human intelligence has been projected onto AI model capability. AI models only have a limited dimension of human intelligence, and in some axes orthogonal, and when I say distillation I refer to this.
You say it like it's a fact, but in reality everyone sees the phenomenon of AI slop.
P.S. Information search and retrieval if the best and most direct way to use LLMs.
Just purely organic YouTube Comments circa early '20s alone surely outslop any "AI" by a giant margin.
Everyone sees the markers, and it's a hot topic. There are maybe a thousand from-scratch trained models, and just few mainstream ones produce most of human-targeted content. In today's world, no surprise everyone knows the common patterns of those. That sloppy landscape is not just load-bearing em-dashes — it's a humble testament to their reinforcement learning.
Humans produce tons of texts, with all sorts of nonsense in it, without thinking it through. Our slop is just a lot more diverse. And mostly just spoken out loud.
> P.S. Information search and retrieval if the best and most direct way to use LLMs.
Yes, but not directly, if they don't know something they tend to hallucinate like mad, even today. YMMV, but in my experience they work best as actual "cheap" reasoning for building queries and checking out search engine results. Even if they misinterpret some result, more and more results will still steer it towards correct conclusions and it can point at some results that relate well enough to be useful.
I agree with your last statement.